Overview

Dataset statistics

Number of variables12
Number of observations5787
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory542.7 KiB
Average record size in memory96.0 B

Variable types

Numeric12

Warnings

avg_recency_days is highly correlated with frequencyHigh correlation
avg_basket_size is highly correlated with returned_revenue and 1 other fieldsHigh correlation
gross_revenue is highly correlated with orders and 1 other fieldsHigh correlation
returned_revenue is highly correlated with avg_basket_size and 1 other fieldsHigh correlation
orders is highly correlated with gross_revenue and 2 other fieldsHigh correlation
qt_products is highly correlated with ordersHigh correlation
qt_items is highly correlated with gross_revenue and 1 other fieldsHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
average_ticket is highly correlated with avg_basket_size and 1 other fieldsHigh correlation
avg_recency_days is highly correlated with orders and 2 other fieldsHigh correlation
avg_basket_size is highly correlated with avg_unique_basket_size and 4 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with avg_basket_size and 2 other fieldsHigh correlation
gross_revenue is highly correlated with avg_basket_size and 4 other fieldsHigh correlation
last_purchase is highly correlated with orders and 1 other fieldsHigh correlation
orders is highly correlated with avg_recency_days and 5 other fieldsHigh correlation
qt_products is highly correlated with avg_basket_size and 5 other fieldsHigh correlation
qt_items is highly correlated with avg_recency_days and 5 other fieldsHigh correlation
frequency is highly correlated with avg_recency_days and 2 other fieldsHigh correlation
average_ticket is highly correlated with avg_basket_size and 4 other fieldsHigh correlation
customer_id is highly correlated with avg_recency_days and 2 other fieldsHigh correlation
avg_recency_days is highly correlated with customer_id and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with returned_revenue and 1 other fieldsHigh correlation
gross_revenue is highly correlated with avg_basket_size and 3 other fieldsHigh correlation
returned_revenue is highly correlated with customer_id and 1 other fieldsHigh correlation
last_purchase is highly correlated with ordersHigh correlation
orders is highly correlated with customer_id and 2 other fieldsHigh correlation
qt_products is highly correlated with avg_unique_basket_size and 2 other fieldsHigh correlation
qt_items is highly correlated with avg_basket_size and 3 other fieldsHigh correlation
frequency is highly correlated with avg_recency_days and 2 other fieldsHigh correlation
average_ticket is highly correlated with avg_basket_size and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with qt_items and 3 other fieldsHigh correlation
qt_items is highly correlated with avg_basket_size and 4 other fieldsHigh correlation
gross_revenue is highly correlated with avg_basket_size and 4 other fieldsHigh correlation
orders is highly correlated with qt_items and 2 other fieldsHigh correlation
returned_revenue is highly correlated with avg_basket_size and 3 other fieldsHigh correlation
qt_products is highly correlated with qt_items and 2 other fieldsHigh correlation
average_ticket is highly correlated with avg_basket_size and 1 other fieldsHigh correlation
customer_id is highly correlated with last_purchaseHigh correlation
last_purchase is highly correlated with customer_idHigh correlation
avg_basket_size is highly skewed (γ1 = 48.87915101) Skewed
gross_revenue is highly skewed (γ1 = 21.78638916) Skewed
returned_revenue is highly skewed (γ1 = 59.94180828) Skewed
qt_items is highly skewed (γ1 = 23.22477286) Skewed
average_ticket is highly skewed (γ1 = 28.00259794) Skewed
customer_id has unique values Unique
avg_recency_days has 3013 (52.1%) zeros Zeros
avg_basket_size has 91 (1.6%) zeros Zeros
avg_unique_basket_size has 91 (1.6%) zeros Zeros
gross_revenue has 91 (1.6%) zeros Zeros
returned_revenue has 4190 (72.4%) zeros Zeros
last_purchase has 129 (2.2%) zeros Zeros
orders has 91 (1.6%) zeros Zeros
qt_products has 91 (1.6%) zeros Zeros
qt_items has 91 (1.6%) zeros Zeros
frequency has 91 (1.6%) zeros Zeros
average_ticket has 91 (1.6%) zeros Zeros

Reproduction

Analysis started2021-08-10 21:17:28.929123
Analysis finished2021-08-10 21:18:04.781082
Duration35.85 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

customer_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct5787
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16640.72162
Minimum12346
Maximum22709
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.3 KiB
2021-08-10T18:18:05.195924image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum12346
5-th percentile12702.3
Q114306.5
median16270
Q318261.5
95-th percentile21774.7
Maximum22709
Range10363
Interquartile range (IQR)3955

Descriptive statistics

Standard deviation2825.014253
Coefficient of variation (CV)0.1697651291
Kurtosis-0.8551246034
Mean16640.72162
Median Absolute Deviation (MAD)1978
Skewness0.4234066713
Sum96299856
Variance7980705.53
MonotonicityNot monotonic
2021-08-10T18:18:05.452961image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
155841
 
< 0.1%
210881
 
< 0.1%
210871
 
< 0.1%
210861
 
< 0.1%
155781
 
< 0.1%
124241
 
< 0.1%
210841
 
< 0.1%
178371
 
< 0.1%
210811
 
< 0.1%
Other values (5777)5777
99.8%
ValueCountFrequency (%)
123461
< 0.1%
123471
< 0.1%
123481
< 0.1%
123491
< 0.1%
123501
< 0.1%
123521
< 0.1%
123531
< 0.1%
123541
< 0.1%
123551
< 0.1%
123561
< 0.1%
ValueCountFrequency (%)
227091
< 0.1%
227081
< 0.1%
227071
< 0.1%
227061
< 0.1%
227051
< 0.1%
227041
< 0.1%
227001
< 0.1%
226991
< 0.1%
226961
< 0.1%
226951
< 0.1%

avg_recency_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1156
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.77016413
Minimum0
Maximum366
Zeros3013
Zeros (%)52.1%
Negative0
Negative (%)0.0%
Memory size45.3 KiB
2021-08-10T18:18:05.695446image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q356.41666667
95-th percentile164
Maximum366
Range366
Interquartile range (IQR)56.41666667

Descriptive statistics

Standard deviation60.58422735
Coefficient of variation (CV)1.604023407
Kurtosis6.556761959
Mean37.77016413
Median Absolute Deviation (MAD)0
Skewness2.356093233
Sum218575.9398
Variance3670.448604
MonotonicityNot monotonic
2021-08-10T18:18:06.082247image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03013
52.1%
7021
 
0.4%
4618
 
0.3%
5517
 
0.3%
4916
 
0.3%
3116
 
0.3%
9116
 
0.3%
2115
 
0.3%
3515
 
0.3%
4215
 
0.3%
Other values (1146)2625
45.4%
ValueCountFrequency (%)
03013
52.1%
19
 
0.2%
24
 
0.1%
2.8615384621
 
< 0.1%
36
 
0.1%
3.3303571431
 
< 0.1%
3.3513513511
 
< 0.1%
45
 
0.1%
4.1910112361
 
< 0.1%
4.2758620691
 
< 0.1%
ValueCountFrequency (%)
3661
 
< 0.1%
3651
 
< 0.1%
3641
 
< 0.1%
3631
 
< 0.1%
3572
< 0.1%
3561
 
< 0.1%
3552
< 0.1%
3521
 
< 0.1%
3512
< 0.1%
3503
0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct2370
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean263.9606991
Minimum0
Maximum74215
Zeros91
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size45.3 KiB
2021-08-10T18:18:06.332677image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q172
median148
Q3288
95-th percentile731.7
Maximum74215
Range74215
Interquartile range (IQR)216

Descriptive statistics

Standard deviation1190.096034
Coefficient of variation (CV)4.508610708
Kurtosis2809.373976
Mean263.9606991
Median Absolute Deviation (MAD)97
Skewness48.87915101
Sum1527540.566
Variance1416328.571
MonotonicityNot monotonic
2021-08-10T18:18:06.723469image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1115
 
2.0%
091
 
1.6%
272
 
1.2%
351
 
0.9%
449
 
0.8%
535
 
0.6%
629
 
0.5%
1226
 
0.4%
7222
 
0.4%
10022
 
0.4%
Other values (2360)5275
91.2%
ValueCountFrequency (%)
091
1.6%
1115
2.0%
272
1.2%
351
0.9%
3.3333333331
 
< 0.1%
449
0.8%
535
 
0.6%
5.3333333331
 
< 0.1%
5.6666666671
 
< 0.1%
629
 
0.5%
ValueCountFrequency (%)
742151
< 0.1%
40498.51
< 0.1%
141491
< 0.1%
139561
< 0.1%
78241
< 0.1%
6009.3333331
< 0.1%
59631
< 0.1%
51971
< 0.1%
43001
< 0.1%
42821
< 0.1%

avg_unique_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1172
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.66374376
Minimum0
Maximum1109
Zeros91
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size45.3 KiB
2021-08-10T18:18:07.041543image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median15
Q330.46428571
95-th percentile171.7
Maximum1109
Range1109
Interquartile range (IQR)23.46428571

Descriptive statistics

Standard deviation76.4113062
Coefficient of variation (CV)2.084110851
Kurtosis33.35657014
Mean36.66374376
Median Absolute Deviation (MAD)10
Skewness5.107288829
Sum212173.0851
Variance5838.687716
MonotonicityNot monotonic
2021-08-10T18:18:07.381355image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1278
 
4.8%
2161
 
2.8%
3115
 
2.0%
9105
 
1.8%
10105
 
1.8%
8103
 
1.8%
6101
 
1.7%
5101
 
1.7%
7101
 
1.7%
1397
 
1.7%
Other values (1162)4520
78.1%
ValueCountFrequency (%)
091
1.6%
0.21
 
< 0.1%
0.253
 
0.1%
0.33333333337
 
0.1%
0.41
 
< 0.1%
0.40909090911
 
< 0.1%
0.512
 
0.2%
0.54545454551
 
< 0.1%
0.55555555561
 
< 0.1%
0.57142857141
 
< 0.1%
ValueCountFrequency (%)
11091
< 0.1%
7481
< 0.1%
7301
< 0.1%
7201
< 0.1%
7031
< 0.1%
6861
< 0.1%
6751
< 0.1%
6731
< 0.1%
6601
< 0.1%
6491
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct5435
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1775.208372
Minimum0
Maximum279138.02
Zeros91
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size45.3 KiB
2021-08-10T18:18:07.674186image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.95
Q1221.125
median599.36
Q31551.68
95-th percentile5230.976
Maximum279138.02
Range279138.02
Interquartile range (IQR)1330.555

Descriptive statistics

Standard deviation7837.600213
Coefficient of variation (CV)4.415031123
Kurtosis617.32801
Mean1775.208372
Median Absolute Deviation (MAD)480.56
Skewness21.78638916
Sum10273130.85
Variance61427977.1
MonotonicityNot monotonic
2021-08-10T18:18:08.056557image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
091
 
1.6%
7.959
 
0.2%
4.958
 
0.1%
2.958
 
0.1%
1.258
 
0.1%
3.757
 
0.1%
12.757
 
0.1%
1.657
 
0.1%
5.956
 
0.1%
7.56
 
0.1%
Other values (5425)5630
97.3%
ValueCountFrequency (%)
091
1.6%
0.421
 
< 0.1%
0.651
 
< 0.1%
0.791
 
< 0.1%
0.844
 
0.1%
0.853
 
0.1%
1.071
 
< 0.1%
1.258
 
0.1%
1.441
 
< 0.1%
1.657
 
0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
168472.51
< 0.1%
140450.721
< 0.1%
124564.531
< 0.1%
117379.631
< 0.1%
91062.381
< 0.1%
77183.61
< 0.1%
72882.091
< 0.1%

returned_revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1122
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.00230171
Minimum0
Maximum168469.6
Zeros4190
Zeros (%)72.4%
Negative0
Negative (%)0.0%
Memory size45.3 KiB
2021-08-10T18:18:08.404358image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35.305
95-th percentile119.473
Maximum168469.6
Range168469.6
Interquartile range (IQR)5.305

Descriptive statistics

Standard deviation2473.930809
Coefficient of variation (CV)29.45075026
Kurtosis3874.877918
Mean84.00230171
Median Absolute Deviation (MAD)0
Skewness59.94180828
Sum486121.32
Variance6120333.647
MonotonicityNot monotonic
2021-08-10T18:18:08.729192image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04190
72.4%
12.7521
 
0.4%
4.9520
 
0.3%
1519
 
0.3%
9.9517
 
0.3%
5.913
 
0.2%
25.511
 
0.2%
3.7510
 
0.2%
4.2510
 
0.2%
19.810
 
0.2%
Other values (1112)1466
 
25.3%
ValueCountFrequency (%)
04190
72.4%
0.422
 
< 0.1%
0.651
 
< 0.1%
0.951
 
< 0.1%
1.256
 
0.1%
1.454
 
0.1%
1.641
 
< 0.1%
1.655
 
0.1%
1.72
 
< 0.1%
1.791
 
< 0.1%
ValueCountFrequency (%)
168469.61
< 0.1%
77183.61
< 0.1%
22998.41
< 0.1%
14688.241
< 0.1%
8511.151
< 0.1%
7443.591
< 0.1%
5228.41
< 0.1%
4815.261
< 0.1%
4814.741
< 0.1%
4486.241
< 0.1%

last_purchase
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct304
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.0687748
Minimum0
Maximum373
Zeros129
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size45.3 KiB
2021-08-10T18:18:09.089988image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q121
median68
Q3198
95-th percentile337
Maximum373
Range373
Interquartile range (IQR)177

Descriptive statistics

Standard deviation111.7168247
Coefficient of variation (CV)0.9708700282
Kurtosis-0.6131776373
Mean115.0687748
Median Absolute Deviation (MAD)60
Skewness0.8315491535
Sum665903
Variance12480.64892
MonotonicityNot monotonic
2021-08-10T18:18:09.378819image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0129
 
2.2%
1110
 
1.9%
4105
 
1.8%
398
 
1.7%
292
 
1.6%
1086
 
1.5%
882
 
1.4%
1779
 
1.4%
979
 
1.4%
778
 
1.3%
Other values (294)4849
83.8%
ValueCountFrequency (%)
0129
2.2%
1110
1.9%
292
1.6%
398
1.7%
4105
1.8%
552
0.9%
778
1.3%
882
1.4%
979
1.4%
1086
1.5%
ValueCountFrequency (%)
37323
0.4%
37223
0.4%
37117
0.3%
3694
 
0.1%
36813
0.2%
36716
0.3%
36615
0.3%
36519
0.3%
36411
0.2%
3627
 
0.1%

orders
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct57
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.416450665
Minimum0
Maximum206
Zeros91
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size45.3 KiB
2021-08-10T18:18:09.655660image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q34
95-th percentile11
Maximum206
Range206
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.772720769
Coefficient of variation (CV)1.982385063
Kurtosis304.9238656
Mean3.416450665
Median Absolute Deviation (MAD)1
Skewness13.23985225
Sum19771
Variance45.86974661
MonotonicityNot monotonic
2021-08-10T18:18:09.902526image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12871
49.6%
2826
 
14.3%
3503
 
8.7%
4394
 
6.8%
5237
 
4.1%
6173
 
3.0%
7138
 
2.4%
898
 
1.7%
091
 
1.6%
969
 
1.2%
Other values (47)387
 
6.7%
ValueCountFrequency (%)
091
 
1.6%
12871
49.6%
2826
 
14.3%
3503
 
8.7%
4394
 
6.8%
5237
 
4.1%
6173
 
3.0%
7138
 
2.4%
898
 
1.7%
969
 
1.2%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
< 0.1%
861
< 0.1%
721
< 0.1%
622
< 0.1%
601
< 0.1%
571
< 0.1%

qt_products
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct530
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.13789528
Minimum0
Maximum7838
Zeros91
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size45.3 KiB
2021-08-10T18:18:10.164368image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q113
median39
Q3104
95-th percentile330.7
Maximum7838
Range7838
Interquartile range (IQR)91

Descriptive statistics

Standard deviation209.218436
Coefficient of variation (CV)2.295625056
Kurtosis515.8908895
Mean91.13789528
Median Absolute Deviation (MAD)32
Skewness17.83370582
Sum527415
Variance43772.35395
MonotonicityNot monotonic
2021-08-10T18:18:10.811774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1256
 
4.4%
2149
 
2.6%
3109
 
1.9%
10101
 
1.7%
699
 
1.7%
992
 
1.6%
591
 
1.6%
091
 
1.6%
487
 
1.5%
783
 
1.4%
Other values (520)4629
80.0%
ValueCountFrequency (%)
091
 
1.6%
1256
4.4%
2149
2.6%
3109
1.9%
487
 
1.5%
591
 
1.6%
699
 
1.7%
783
 
1.4%
881
 
1.4%
992
 
1.6%
ValueCountFrequency (%)
78381
< 0.1%
56731
< 0.1%
50951
< 0.1%
45801
< 0.1%
26981
< 0.1%
23791
< 0.1%
20601
< 0.1%
18181
< 0.1%
16731
< 0.1%
16371
< 0.1%

qt_items
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1842
Distinct (%)31.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean963.0902022
Minimum0
Maximum196844
Zeros91
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size45.3 KiB
2021-08-10T18:18:11.110602image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q1100
median308
Q3797
95-th percentile2897.5
Maximum196844
Range196844
Interquartile range (IQR)697

Descriptive statistics

Standard deviation4395.384863
Coefficient of variation (CV)4.563835094
Kurtosis797.1697164
Mean963.0902022
Median Absolute Deviation (MAD)252
Skewness23.22477286
Sum5573403
Variance19319408.09
MonotonicityNot monotonic
2021-08-10T18:18:11.389442image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1114
 
2.0%
091
 
1.6%
273
 
1.3%
351
 
0.9%
449
 
0.8%
535
 
0.6%
629
 
0.5%
1225
 
0.4%
8822
 
0.4%
7221
 
0.4%
Other values (1832)5277
91.2%
ValueCountFrequency (%)
091
1.6%
1114
2.0%
273
1.3%
351
0.9%
449
0.8%
535
 
0.6%
629
 
0.5%
720
 
0.3%
818
 
0.3%
97
 
0.1%
ValueCountFrequency (%)
1968441
< 0.1%
809971
< 0.1%
802631
< 0.1%
773731
< 0.1%
742151
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
633121
< 0.1%
583431
< 0.1%

frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1227
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53859758
Minimum0
Maximum17
Zeros91
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size45.3 KiB
2021-08-10T18:18:11.703267image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00978156735
Q10.02380952381
median1
Q31
95-th percentile1
Maximum17
Range17
Interquartile range (IQR)0.9761904762

Descriptive statistics

Standard deviation0.5501400023
Coefficient of variation (CV)1.021430513
Kurtosis137.3129067
Mean0.53859758
Median Absolute Deviation (MAD)0.6
Skewness4.81623109
Sum3116.864195
Variance0.3026540221
MonotonicityNot monotonic
2021-08-10T18:18:12.022079image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12879
49.7%
091
 
1.6%
247
 
0.8%
0.062518
 
0.3%
0.0277777777817
 
0.3%
0.0238095238116
 
0.3%
0.0833333333315
 
0.3%
0.0909090909115
 
0.3%
0.0344827586214
 
0.2%
0.0294117647114
 
0.2%
Other values (1217)2661
46.0%
ValueCountFrequency (%)
091
1.6%
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055865921792
 
< 0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
 
< 0.1%
0.005665722381
 
< 0.1%
0.0056818181822
 
< 0.1%
ValueCountFrequency (%)
171
 
< 0.1%
41
 
< 0.1%
35
 
0.1%
247
 
0.8%
1.1428571431
 
< 0.1%
12879
49.7%
0.751
 
< 0.1%
0.66666666673
 
0.1%
0.5508021391
 
< 0.1%
0.53351206431
 
< 0.1%

average_ticket
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct5448
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean572.8375319
Minimum0
Maximum84236.25
Zeros91
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size45.3 KiB
2021-08-10T18:18:12.309404image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.95
Q1154.0867045
median291.2078571
Q3480.6578571
95-th percentile1830.412
Maximum84236.25
Range84236.25
Interquartile range (IQR)326.5711526

Descriptive statistics

Standard deviation2025.824087
Coefficient of variation (CV)3.536472339
Kurtosis1001.662008
Mean572.8375319
Median Absolute Deviation (MAD)152.6828571
Skewness28.00259794
Sum3315010.797
Variance4103963.23
MonotonicityNot monotonic
2021-08-10T18:18:12.558279image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
091
 
1.6%
7.959
 
0.2%
2.958
 
0.1%
4.958
 
0.1%
1.258
 
0.1%
12.757
 
0.1%
1.657
 
0.1%
3.757
 
0.1%
4.256
 
0.1%
7.56
 
0.1%
Other values (5438)5630
97.3%
ValueCountFrequency (%)
091
1.6%
0.421
 
< 0.1%
0.651
 
< 0.1%
0.791
 
< 0.1%
0.844
 
0.1%
0.853
 
0.1%
1.071
 
< 0.1%
1.258
 
0.1%
1.441
 
< 0.1%
1.657
 
0.1%
ValueCountFrequency (%)
84236.251
< 0.1%
77183.61
< 0.1%
52940.941
< 0.1%
50653.911
< 0.1%
21389.61
< 0.1%
18745.861
< 0.1%
14855.531
< 0.1%
14844.766671
< 0.1%
13305.51
< 0.1%
12681.581
< 0.1%

Interactions

2021-08-10T18:17:36.209884image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:36.495178image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:36.680082image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:36.864976image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:37.043308image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:37.201630image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:37.390960image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:37.552205image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:37.717115image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:37.902447image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:38.080415image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:38.249696image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:38.433582image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:38.618591image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:38.803521image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:38.988276image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:39.166716image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:39.353053image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:39.535125image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:39.731819image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:39.905689image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:40.090288image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:40.284359image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:40.567914image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:40.754811image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:41.024354image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:41.357880image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:41.619413image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:41.803865image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:41.995566image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:42.223206image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:42.416741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:42.573473image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:42.789412image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:42.994543image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:43.203579image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:43.381743image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:43.541633image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:43.720452image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:43.879773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:44.053268image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:44.226136image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:44.398409image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:44.569947image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:44.731323image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:44.906618image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:45.081089image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:45.264678image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:45.426353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:45.607096image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:45.788745image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:46.115810image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:46.269522image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:46.438819image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:46.617264image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:46.802238image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:46.954960image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:47.127271image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:47.314271image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:47.486496image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:47.645955image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:47.819508image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:48.005860image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:48.192583image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:48.364191image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:48.536574image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:48.736168image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:48.951563image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:49.137044image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:49.322013image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:49.518565image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:49.709937image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:49.880370image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:50.058374image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:50.238437image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:50.432349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:50.614895image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:50.787735image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:50.972708image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:51.165704image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:51.329618image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:51.511242image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:51.701647image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:51.905413image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:52.087140image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:52.428351image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:52.591152image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:52.757279image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:52.914026image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:53.079484image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:53.337376image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:53.527631image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:53.718539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:53.916410image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:54.102266image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:54.273696image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:54.433978image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:54.602954image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:54.792009image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:55.018589image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:55.226177image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:55.436602image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:55.643042image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:55.845417image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:56.049554image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:56.247430image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:56.459091image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:56.647202image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:56.908580image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:57.097415image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:57.292913image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:57.499230image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:57.684184image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:57.868175image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:58.060651image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:58.252837image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:58.425011image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:58.630592image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:58.842789image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:59.034890image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:59.255944image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:59.451308image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:59.651780image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:17:59.843066image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:18:00.061955image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:18:00.238280image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:18:00.639265image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:18:00.824238image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:18:01.018670image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:18:01.212094image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:18:01.402935image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:18:01.593949image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:18:01.786307image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:18:01.933797image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:18:02.105899image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:18:02.284860image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:18:02.447660image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:18:02.617692image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:18:02.844445image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:18:03.085646image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:18:03.244718image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:18:03.428549image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:18:03.628417image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-10T18:18:03.802103image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-08-10T18:18:12.795642image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-10T18:18:13.183890image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-10T18:18:13.546656image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-10T18:18:13.931436image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-08-10T18:18:04.161726image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-10T18:18:04.587686image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

customer_idavg_recency_daysavg_basket_sizeavg_unique_basket_sizegross_revenuereturned_revenuelast_purchaseordersqt_productsqt_itemsfrequencyaverage_ticket
0178501.00000050.9705880.6176475391.21102.58372.034.0297.01733.017.000000158.565000
11304752.833333154.44444411.6666673232.59143.4956.09.0171.01390.00.028302359.176667
21258326.500000335.2000007.6000006705.3876.042.015.0232.05028.00.040323447.025333
31374892.66666787.8000004.800000948.250.0095.05.028.0439.00.017921189.650000
41510020.00000026.6666670.333333876.00240.90333.03.03.080.00.073171292.000000
51529126.769231150.1428574.3571434623.3071.7925.014.0102.02102.00.040115330.235714
61468819.263158172.4285717.0476195630.87523.497.021.0327.03621.00.057221268.136667
71780939.666667171.4166673.8333335411.9167.0616.012.061.02057.00.033520450.992500
8153114.191011419.7142866.23076960767.901348.560.091.02379.038194.00.243316667.779121
91609847.66666787.5714294.8571432005.630.0087.07.067.0613.00.024390286.518571

Last rows

customer_idavg_recency_daysavg_basket_sizeavg_unique_basket_sizegross_revenuereturned_revenuelast_purchaseordersqt_productsqt_itemsfrequencyaverage_ticket
5777227000.01074.055.04839.420.01.01.062.01074.01.04839.42
5778132980.096.02.0360.000.01.01.02.096.01.0360.00
5779145690.079.010.0227.390.01.01.012.079.01.0227.39
5780227040.014.07.017.900.01.01.07.014.01.017.90
5781227050.02.02.03.350.01.01.02.02.01.03.35
5782227060.01747.0634.05699.000.01.01.0634.01747.01.05699.00
5783227070.02010.0730.06756.060.00.01.0730.02010.01.06756.06
5784227080.0654.056.03217.200.00.01.059.0654.01.03217.20
5785227090.0731.0217.03950.720.00.01.0217.0731.01.03950.72
5786127130.0505.037.0794.550.00.01.037.0505.01.0794.55